ARMA-Based Adaptive Coding Transmission Over Millimeter-Wave Channel for Integrated Satellite-Terrestrial Networks

Gu, Shushi; Jiao, Jian; Huang, Zixuan; Wu, Shaohua; Zhang, Qinyu

doi:10.1109/access.2018.2825256

Cited by 22 publications

(7 citation statements)

References 33 publications

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“…where κ and ν denote the linear model parameters, θ is accounting for variances in shadowing fading and d m denotes the distance between R k * and D m . Following NOMA procedures [42], the received signal to interference and noise ratio (SINR) at m-th device to detect the information of q-th device (m > q) is given as follows (7), shown at the bottom of the next page. In which, ηm =…”

Section: System Modelmentioning

confidence: 99%

“…Ka/Q/Vband) [6]. They effectively provide system throughput and extensive coverage of the terrestrial wireless network [7], [8]. However, using the traditional orthogonal multiple access (OMA) technique into SatCom would result in a waste of block resources, such as the time / frequency / code block due to the limitation in the number of simultaneously connected users, the ability to meet for the services requires low latency and high throughput [9], [10].…”

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Performance Analysis of NOMA-Based Hybrid Satellite-Terrestrial Relay System Using mmWave Technology

Nguyen

et al. 2023

IEEE Access

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This paper investigates the performance of NOMA-based hybrid Satellite-Terrestrial relays system (HSTR) using the millimeter wave (mmWave) technology. Furthermore, the relays are equipped with multiple antennas and utilize the amplify and forward (AF) protocol to forward the satellite's superimposed information to multiple destinations. Then, the rain coefficient is considered as the fading factor of the mmWave band to choose the best relay. We considered the shadowed-Rician fading and Nakagami-m fading for satellite links and terrestrial links respectively, and in addition, we evaluated the shadowing effect for satellite links with two modes of: frequent heavy shadowing (FHS) and average shadowing (AS). With these suggestions, the closed-form outage probability (OP) and approximate ergodic capacity (EC) are derived to evaluate the efficiency of the proposed system. Next contribution of the research is an asymptotic analysis for the OP, which is derived in order to gain additional insight into important system parameters. Finally, the theoretical derivation is validated through simulation results and analyzed the impact of significant parameters. These results demonstrate NOMA's superiority to the traditional orthogonal multiple access (OMA) method in the proposed system.INDEX TERMS NOMA, hybrid satellite-terrestrial relay system, millimeter wave, rain attenuation, outage probability, ergodic capacity. I. INTRODUCTIONRecently, satellite communication (SatCom) has been one of the potential technologies for the fifth generation (5G) network and beyond, which brings many advantages such as high throughput, great reliability, extensive coverage, inexpensive operations, and energy-efficient [1], [2], [3]. Therefore, the integration of SatCom into current terrestrial communication systems has received considerable attentionThe associate editor coordinating the review of this manuscript and approving it for publication was Abdel-Hamid Soliman .

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Section: System Modelmentioning

confidence: 99%

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confidence: 99%

Performance Analysis of NOMA-Based Hybrid Satellite-Terrestrial Relay System Using mmWave Technology

Nguyen

et al. 2023

IEEE Access

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“…Ekerete et al [ 39 ] investigated adaptive margins for AMC in broadband satellite links during the actual rain event. To increase system throughput and improve transmission efficiency, an adaptive coding transmission (ACT) scheme was proposed in Reference [ 40 ] over the satellite-terrestrial channel based on the analog fountain code (AFC), achieving a seamless performance across all channel states.…”

Section: Related Workmentioning

confidence: 99%

Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks

Cen

Liu

et al. 2021

Sensors

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The communication channel in underwater acoustic sensor networks (UASNs) is time-varying due to the dynamic environmental factors, such as ocean current, wind speed, and temperature profile. Generally, these phenomena occur with a certain regularity, resulting in a similar variation pattern inherited in the communication channels. Based on these observations, the energy efficiency of data transmission can be improved by controlling the modulation method, coding rate, and transmission power according to the channel dynamics. Given the limited computational capacity and energy in underwater nodes, we propose a double-scale adaptive transmission mechanism for the UASNs, where the transmission configuration will be determined by the predicted channel states adaptively. In particular, the historical channel state series will first be decomposed into large-scale and small-scale series and then be predicted by a novel k-nearest neighbor search algorithm with sliding window. Next, an energy-efficient transmission algorithm is designed to solve the problem of long-term modulation and coding optimization. In particular, a quantitative model is constructed to describe the relationship between data transmission and the buffer threshold used in this mechanism, which can then analyze the influence of buffer threshold under different channel states or data arrival rates theoretically. Finally, numerical simulations are conducted to verify the proposed schemes, and results show that they can achieve good performance in terms of channel prediction and energy consumption with moderate buffer length.

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“…At the same time, the satellite communications, machine to machine communications and sensor technology are made breakthrough [2]. Thus, the satellite-based Internet of Things (S-IoT) with next generation of high throughput satellite (HTS) which can provide broadband access in a global coverage and cost-effective manner [3], is viewed as a vital role to address issues related to human living such as energy management, climate change, transportation, healthcare, business…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Long-Term Power Allocation Scheme for NOMA Downlink System in S-IoT

et al. 2019

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In this paper, we formulate a long-term resource allocation problem of non-orthogonal multiple access (NOMA) downlink system for the satellite-based Internet of Things (S-IoT) to achieve the optimal decoding order and power allocation. This long-term resource allocation problem of the satellite NOMA downlink system can be decomposed into two subproblems, i.e., a rate control subproblem and a power allocation subproblem. The latter is a non-convex problem and the solution of which relies on both queue state and channel state. However, the queue state and the channel state continually change from one time slot to another, which makes it extremely strenuous to characterize the optimal decoding order of successive interference cancellation (SIC). Therefore, we explore the weight relationship between the queue state and the channel state to derive an optimal decoding order by leveraging deep learning. The proposed deep learning-based long-term power allocation (DL-PA) scheme can efficiently derive a more accurate decoding order than the conventional solution. The simulation results show that the DL-PA scheme can improve the performance of the S-IoT NOMA downlink system, in terms of long-term network utility, average arriving rate, and queuing delay. INDEX TERMS Satellite-based Internet of Things, deep learning, non-orthogonal multiple access, successive interference cancellation.

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ARMA-Based Adaptive Coding Transmission Over Millimeter-Wave Channel for Integrated Satellite-Terrestrial Networks

Cited by 22 publications

References 33 publications

Performance Analysis of NOMA-Based Hybrid Satellite-Terrestrial Relay System Using mmWave Technology

Performance Analysis of NOMA-Based Hybrid Satellite-Terrestrial Relay System Using mmWave Technology

Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks

Deep Learning-Based Long-Term Power Allocation Scheme for NOMA Downlink System in S-IoT

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